scholarly journals Embedded Smart Car Security System on Face Detection

2014 ◽  
pp. 176-180
Author(s):  
Vikram Kulkarni ◽  
Viswaprakash Babu
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Security System ◽  
Experimental Results ◽  
Embedded Platform ◽  
Web Camera ◽  
The Face ◽  
Car Security ◽  
System Prototype

In this proposed embedded car security system, FDS(Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined images if the image doesn’t match, then the information is sent to the owner through MMS. So now owner can obtain the image of the thief in his mobile as well as he can trace the location through GPS. The location of the car as well as its speed can be displayed to the owner through SMS. So by using this system, owner can identify the thief image as well as the location of the car This system prototype is built on the base of one embedded platform in which one SoC named “SEP4020”(works at 100MHz) controls all the processes .Experimental results illuminate the validity of this car security system.

scholarly journals PEMODELAN SISTEM DETEKSI WAJAH SEBAGAI PENGHITUNG JUMLAH PENUMPANG TRANSPORTASI PUBLIK

2021 ◽  
Vol 4 (1) ◽  
pp. 67-77
Author(s):  
Fransiska Sisilia Mukti ◽  
Lia Farokhah ◽  
Nur Lailatul Aqromi
Keyword(s):  
Embedded System ◽  
System Design ◽  
Face Detection ◽  
Public Transportation ◽  
Detection System ◽  
Raspberry Pi ◽  
The Internet ◽  
Detection Mechanism ◽  
Information System Design ◽  
The Face

Bus is one of public transportation and as the most preferable by Indonesian to support their mobility. The high number of bus traffics then demands the bus management to provide the maximum service for their passenger, in order to gain public trust. Unfortunately, in the reality passenger list’s fraud is often faced by the bus management, there is a mismatch list between the amount of deposits made by bus driver and the number of passengers carried by the bus, and as the result it caused big loss for the Bus management. Automatic Passenger Counting (APC) then as an artificial intelligence program that is considered to cope with the bus management problems. This research carried out an APC technology based on passenger face detection using the Viola-Jones method, which is integrated with an embedded system based on the Internet of Things in the processing and data transmission. To detect passenger images, a webcam is provided that is connected to the Raspberry pi which is then sent to the server via the Internet to be displayed on the website provided. The system database will be updated within a certain period of time, or according to the stop of the bus (the system can be adjusted according to management needs). The system will calculate the number of passengers automatically; the bus management can export passenger data whenever as they want. There are 3 main points in the architecture of modeling system, they are information system design, device architecture design, and face detection mechanism design to calculate the number of passengers. A system design test is carried out to assess the suitability of the system being built with company needs. Then, based on the questionnaire distributed to the respondent, averagely 85.12 % claim that the Face detection system is suitability. The score attained from 4 main aspects including interactivity, aesthetics, layout and personalization

Efficient Half Face Detection System Based on Linear Binary Pattern

2020 ◽  
Vol 17 (5) ◽  
pp. 2342-2348
Author(s):  
Ashutosh Upadhyay ◽  
S. Vijayalakshmi
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Data Set ◽  
Computation Cost ◽  
Face Features ◽  
Positive Rate ◽  
The Face

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

Face Detection System Based on SIFT Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1035-1037
Author(s):  
Ming Zhao Li ◽  
Hong Wei Zhao
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Detection Function ◽  
Sift Algorithm ◽  
The Face

This paper consists of three chapters, first introduced the algorithm and methods of Face detection, and then focus on the SIFT algorithm, finally use SIFT achieve the face detection function.

scholarly journals Robot movement controller based on dynamic facial pattern recognition

2021 ◽  
Vol 22 (2) ◽  
pp. 733
Author(s):  
Siti Nurmaini ◽  
Ahmad Zarkasi ◽  
Deris Stiawan ◽  
Bhakti Yudho Suprapto ◽  
Sri Desy Siswanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Face Detection ◽  
Detection System ◽  
Accuracy Rate ◽  
True Value ◽  
The Face ◽  
Facial Pattern ◽  
Lower Frame ◽  
The Right

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals An Efficient Method for Multiple Face Detection & Recognition

2019 ◽  
Vol 9 (1S3) ◽  
pp. 474-478
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Efficient Method ◽  
Recognition Rate ◽  
The Future ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Future Work ◽  
Detection And Recognition

The problem of Face detection and recognition is becoming a challenge due to the wide variety of faces and the complexity of the noises and background of image. In this paper we have used C-sharp and Haar algorithm to detect the face. First in this paper the image is taken with a web-camera, storing it in the database and then once again when the person comes in the frame the name of the person is displayed. This paper is done in C-sharp which was a bit difficult for us to do and we have combined both the face detection and the recognition. The proposed method has good output and a good recognition rate. The limitation of the paper is that it does not display the name of the person above the face. In the future work will be carried on the above said topic. While developing the code some sample codes in python but those were basic programs. So this paper aims to find a solution for it and developed in C-sharp. In finding the XML file of haarcascade frontal face detection we found some problems and had to do a bit of research in finding it. The code for face detection and face recognition were found in different places and in this paper the codes for the both has been combined and found some difficulty. To overcome the basic programs we have written the code in C-sharp and the difficulty which we faced in combining the two codes have been solved. The solution has been successfully implemented and the code is fully running and the output has been successfully achieved.

scholarly journals Face Detection in a Mixed-Subject Document

2016 ◽  
Vol 6 (6) ◽  
pp. 2828
Author(s):  
Lhoussaine Bouhou ◽  
Rachid El Ayachi ◽  
Mohamed Baslam ◽  
Mohamed Oukessou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Detection System ◽  
Second Phase ◽  
Text And Image ◽  
Important Research Topic ◽  
The Face ◽  
Second Category ◽  
Text Images

<p>Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies  related  to  this  topic  which  have  focused  on  images  inputs  data  faces,  we  are  more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image.</p>

A Realization of Face Detection System Based on ARM Linux

2013 ◽  
Vol 333-335 ◽  
pp. 864-867 ◽  
Author(s):  
Cong Ting Zhao ◽  
Hong Yun Wang ◽  
Jia Wei Li ◽  
Zi Lu Ying
Keyword(s):  
Embedded System ◽  
Monitoring System ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Monitoring ◽  
Adaboost Algorithm ◽  
Arm Processor ◽  
The Face ◽  
Detection Effect

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

ADDRESSING THE PROBLEMS OF DETECTING FACES WITH NEURAL NETWORKS

2007 ◽  
Vol 07 (04) ◽  
pp. 617-640 ◽  
Author(s):  
KEVIN CURRAN ◽  
NEIL McCAUGHLEY ◽  
XUELONG LI
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sequences ◽  
Database Management System ◽  
Surveillance Systems ◽  
Human Computer Interface ◽  
Human Machine Interaction ◽  
The Face ◽  
Machine Interaction

The face is the most distinctive and widely used key to a person's identity. The area of face detection has attracted considerable attention in the advancement of human-machine interaction as it provides a natural and efficient way to communicate between humans and machines. The problem of facial parts in image sequences has become a popular area of research due to emerging applications in intelligent human-computer interface, surveillance systems, content-based image retrieval, video conferencing, financial transaction, forensic applications, pedestrian detection, image database management system and so on. This paper presents the results of an image based neural network face detection system which seeks to address the problem of detecting faces under gross variations.

scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

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